The invention relates to a computed tomography method according to the preamble of claim 1, a computer software, a computing device and computed tomography system.
In computed tomography it is a common problem that the inability to distinguish artifacts from real irregularities and structures in the reconstructed volume data can cause misinterpretation of the reconstructed volume data. This can lead for example to detection problems with automated analysis algorithms applied to the reconstructed volume data. The above problem can generally be addressed by determining the quality of the volume data, which is usually done by comparing the reconstructed volume data to pre-stored ideal volume data of an ideal sample taken from a data base. However, this requires knowledge of the material and/or geometry of the sample under inspection, as well as a complex comparing technique. Furthermore, the comparing step itself introduces a further source of artifacts because it is not possible to warp the reconstructed volume data into prefect voxel-to-voxel alignment with the ideal volume data.
G. Fichtinger et al., “Approximate Volumetric Reconstruction from Projected Images”, MICCAI 2001, vol. LNCS, no. 2208, p. 1376 discloses a technique for approximate reconstruction for angiography where a surgeon draws silhouettes of a target object in 2D images. The silhouettes are back-projected and from the back-projections of the silhouettes a closest fitting shape covering the object in 3D is determined. Excess parts are carved off using forward projections under the condition that the object should fit inside all silhouettes. Finally, the obtained object is projected forward on to each image plane, where the shadow of the reconstructed object is compared to the silhouettes drawn by the surgeon, so that confidence and consistency of silhouette lines can be calculated and visually interpreted. In this manner, a global measure of the form of the whole drawn silhouette is provided as an indication whether the surgeons' drawings are consistent in all images.
U.S. Pat. No. 6,768,782 B1 discloses a reconstruction method for a CT imaging system where differences between the forward projection samples and the measured projections are used as a basis for updating the reconstructed image and a global optimality of an image is measured from a match of the forward projected image to the measured data. Iterations are aborted depending on a global convergence measure.
WO 99 01065 A1 discloses an iterative cone-beam CT reconstruction method where forward projections of reconstructed data are compared to the originally measured projections.
WO 2006 018793 A1, US 2005 105679 A1, WO 2007 150037 A2, WO 2004 100070 A1 and US 2006 104410 A1 disclose related CT reconstruction methods.
An object of the invention is to provide a computed tomography method capable of generating accurate quality information of the reconstructed volume data, in particular allowing further evaluation of the reconstructed volume data with improved reliability.
Embodiments of the invention solve this object with the features of the independent claims. By calculating individual confidence measures for single voxels of the volume data, the quality and therefore the accuracy of the volume data quality information can be significantly enhanced. A confidence measure, or quality measure, of a particular voxel is a value unambiguously related to the probability that the density value of that voxel is correct, or that it is equal to a pre-defined density value. Alternatively the confidence measure may be related to the variance of the voxel density, the probability that the density value of that voxel is incorrect, an error in the voxel density, deviation to the true density, or the voxel accuracy. The confidence measure of a voxel gives quantitative information about the quality of the reconstructed voxel density. The entity of confidence measures over all voxels results in a confidence measure distribution, or confidence measure map, for the whole reconstructed sample volume.